With the mySAP SCM optimizers, you can
see, on-screen and in graphical format, how to reduce production
and transportation costs, increase through-put, deliver higher service
levels, and ultimately improve product margins and your return on assets
at all three planning levels (see Table 1). Optimizers are available
at each level: strategic (long-term analysis and simulation), tactical
(mid-term planning over the entire supply chain), and operational (short-term,
day-to-day operations).

Level

Optimizer

Function

Strategic

Supply Chain Design

Optimizes the placement and capacities
of plants, warehouses, and distribution centers in the supply chain
using a suite of algorithms: metaheuristics based on Voronoi diagrams
and mixed-integer linear programming. Equips companies to project
ideal supply chain networks based on costs and profits, and to make
outsourcing decisions.

Tactical

Supply Planning

Optimizes the global supply chain,
from distribution centers to plants and suppliers. Automatically processes
bills of materials while taking capacities into account, and optimizes
transportation, production, and storage costs, along with revenues
from demand. Uses linear or mixed-integer linear programming. Large-scale
problems can be handled via several decomposition techniques (time,
product, and priority decomposition).

Deployment

Based on production quantities, this
optimizer maximizes service levels while minimizing transportation
costs. In a "fair-share" scenario (where there is less production
than demand), high-priority demands are given preference. In a "push"
scenario (where there is more production than demand), the range of
coverage is balanced in the stocks of the warehouse in the supply
chain.

Operational

Transportation Planning
and Vehicle Scheduling

Optimizes routes and schedules for
pickup of orders at multiple depots, while considering multiple constraints:
vehicle breaks, vehicle capacity constraints, and opening hours at
depot and customer locations. It optimizes routes to minimize the
total delivery cost, and can calculate costs using several dimensions
(e.g., time, distance, stopoffs). By using appropriate cost dimensions,
FTL/LTL costs can be modeled.

By setting penalties for early or late delivery, the optimizer takes
into account requested arrival dates. Allows routing and dispatching
of problems with several hundred orders. Includes a set of metaheuristics,
such as tabu search, that guide local search methods and find optimized
solutions for planning objectives. Heuristics use the runtime determined
by the user and return the best solution found.

Production Planning and
Detailed Scheduling

A multi-objective optimizer that schedules
orders according to manufacturing constraints. Handles complex environments
with alternative routings and resources, secondary resources, and
multi-stage production. Criteria such as service level (delay), setup
time, setup costs, resource costs, and storage costs can be weighted
in the objective function of the optimization. Users can customize
the optimizer using several decomposition strategies based on one
of two basic scheduling optimizers - the Constraint Programming (CP)
or Genetic Algorithm (GA) optimizer - for flexible, fast, and efficient
solutions, even in high-volume environments.

Campaign Optimization

Optimizes the tradeoff between setup
costs/times and inventory costs in manufacturing environments. Particularly
suited to chemical, steel, and paper industries. Based on a two-phase
approach: first, focusing on bottlenecked resources, the campaigns
are optimized; second, optimizing the scheduling of these campaigns,
taking into consideration all production levels.

Model Mix Optimization

Allows production planners to determine
the optimal order and scheduling sequence for manufacturing with a
large number of variants. Various constraints are taken into account,
including quantity and interval constraints, and assignment to a line
or line segment. Especially suited to automotive and high-tech industries.

Table 1

mySAP SCM Optimizer for the Three Levels of Supply Chain Planning.

Suppose your supply chain planner was looking
at his planning table in SAP APO (Figure 1) and sees that the workload
planned for June 26-30, indicated by the red box (all orders with a 30.06.2001
due date), does not actually fit into that time period. In fact, some
orders start on the reactors the week before. He can improve the plan
by:

Requiring that the earliest starting time of orders be 26.06.2001

Minimizing the due date delay (i.e., service level)

Minimizing the setup time and lead time (i.e., resource utilization)

Figure 1

Resource Planning in APO DS

So he selects the "Optimizer"
button in the toolbar to call the PP/DS optimizer. He then adjusts the
parameter settings by selecting an optimizer engine ("Genetic Algorithm"),1
indicates the weighting factors for the objective function (e.g., factor
"10" for setup and lead time, "20" for delay), specifies
the al-lowed optimization runtime, and starts the optimization run (Figure
2).

The "Status" information in Figure
2 reports on the progress of the optimization, indicating three steps:

Reading the problem from liveCache.

Searching (i.e., generating) a first solution.

Improving the solution by continuing to optimize the solution until
the runtime limit is reached.

Figure 2

Using the PP/DS Optimizer to Adjust the Supply Chain Model

With the improved result shown in Figure
3, all orders fit now in the planned time period, even leaving some
spare time on Friday afternoon (perhaps allowing some employees to leave
a little early for the weekend!).

Figure 3

The New Planning Model, After Optimization

Supply Chain Support - No Matter How Complex the Planning
Problem

Guided by a global objective function based on KPIs (for example, total
setup costs, delay penalties, and transportation and production costs),
optimizers generate and evaluate thousands of alternatives. If the optimization
scenario is fairly straightforward - if, for instance, you request a linear
optimiza-tion to be modeled in the Supply Planning component - it is solved
optimally.

In the case of highly complex planning
scenarios, such as a production scheduling problem using alternative resources
on several production stages, the optimizers are scalable to the runtime
limit set by the supply chain planner. In other words, increasing the
CPU time gradually improves the optimization result.

For large, complex optimization problems,
the runtime required to find the true optimum can be prohibitive. However,
SAP's optimization suite gets around this by using several decomposition
strategies. Decomposition "breaks down" the problem into smaller
chunks, so the optimization suite can seamlessly bridge the gap between
fast, rule-based, "greedy" heuristics and a truly optimized
solution.

Limited Computing Resources Need Not Stand in the Way
of Optimization

With all the benefits that mySAP SCM has to offer, supply chain optimization
can still be limited by available computing power. mySAP SCM can compensate
for less computing power by performing a more severe decomposition; however,
this may decrease the quality of the generated solutions. To avoid this
kind of degradation, mySAP SCM offers you the flexibility to address some
of these issues at the source:

If limited computing resources are the result of multiple users working
in parallel, this problem is alleviated within mySAP SCM itself, by
a three-tier client/server architecture configurable for multiprocessor
servers.

Separating the application servers can also help. We suggest running
SAP's liveCache2 and the optimizer on separate
servers. This architecture is especially recommended for modeling a
large supply chain planning problem, since both liveCache and the optimizer
require high-level computing power and high main-memory capacity.3

It is even possible to configure the system with several optimizer
servers or with multi-processor architectures.

Optimization and decomposition techniques
offer great potential for parallelization, which optimizes separate parts
of the supply chain in parallel sessions to reduce the load on the computing
resources. With the multi-user capability of mySAP SCM, the production
planner has the option to parallelize the optimization via a user-defined
script.

Moreover, with the most recent release
of mySAP SCM, Release 3.0, the inherent parallelization of multiple agents
may be used on a multi-processor server to solve large-scale optimization
problems. This approach is already integrated into mySAP SCM decomposition
techniques, as follows: For each decomposed planning problem, different
agents may run in parallel. These agents may use different types of objective
functions or basic optimizers. For a particular user-defined multi-criteria
objective, we may define several agents by doubling, for each agent, the
focus on a single criterion - one agent might double the setup costs focusing
on resource utilization, while another doubles the delay costs focusing
on service level. After completing such an optimization run using several
agents, the user may select one solution from a set of solutions that
have similar overall quality but that differ in a single criterion.

Customizing the Supply Chain Optimization Process

mySAP SCM's optimizer suite offers a generic solution for optimizing
the supply chain that can be configured to master specific business scenarios.
Because mySAP SCM optimizers have an open architecture, users can embed
new basic optimizers and metaheuristics. Possible future enhancements
include model functionality, improved optimization algorithms (libraries),
and higher degrees of parallelization.

The only limits of this algorithmic approach
are computing power and the complexity of the supply chain itself. For
large-scale supply chain problems, the user's challenge is to scale down
planning complexity by taking advantage of the integrated solution for
all three planning levels, as highlighted back in Table 1. The administrator's
challenge is to maximize the enterprise's computing power to exploit the
parallelism of a multiprocessor system. In the end, this dual approach
will allow you to fully exploit the powerful capabilities of the mySAP
SCM solution.

Dr. Heinrich Braun is the Development Manager for optimization algorithms
in the mySAP Supply Chain Management solution. He is also a member of
the computer science faculty at the University of Karlsruhe (independent
lecturer), lecturing on combinatorial optimization problems. Prior to
joining SAP, he led a research group on evolutionary algorithms and neural
networks at the University of Karlsruhe from 1990 to 1996.

1 The basic scheduling optimizers are the CP
(Constraint Programming) or GA (Genetic Algorithm) approaches. The CP
approach is a more generic scheduling optimizer, while the GA scheduler
is based on an evolutionary approach for generating a new supply chain
solution.

2 liveCache is an SAP object database in main
memory, designed especially for planning problems with vast amounts of
data. It is included in SNP, PP/DS, ATP, and TP/VS modules as of mySAP
SCM, Release 3.0.

3 However, if absolutely necessary, smaller
companies can use a single application server for both liveCache and the
optimizer.

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